Add to ORKGWe consider continuous state, continuous action batch reinforcement learning where the goal is to learn a good policy from a sufficiently rich trajectory generated by some policy. We study a variant of fitted Q-iteration, where the greedy action selection is replaced by searching for a policy in a restricted set of candidate policies by maximizing the average action values. We provide a rigorous analysis of this algorithm, proving what we believe is the first finite-time bound for value-function based algorithms for continuous state and action problems.
The choice of the control frequency of a system has a relevant impact on the ability of reinforcemen...
The choice of the control frequency of a system has a relevant impact on the ability of reinforcemen...
The choice of the control frequency of a system has a relevant impact on the ability of reinforcemen...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
International audienceWe consider continuous state, continuous action batch reinforcement learning w...
Abstract — We consider batch reinforcement learning problems in continuous space, expected total dis...
In the field of Reinforcement Learning, Markov Decision Processes with a finite number of states and...
We consider batch reinforcement learning problems in continuous space, expected total discounted-rew...
International audienceThis paper establishes the link between an adaptation of the policy iteration ...
International audienceThis paper establishes the link between an adaptation of the policy iteration ...
International audienceThis paper establishes the link between an adaptation of the policy iteration ...
International audienceThis paper establishes the link between an adaptation of the policy iteration ...
The choice of the control frequency of a system has a relevant impact on the ability of reinforcemen...
The choice of the control frequency of a system has a relevant impact on the ability of reinforcemen...
The choice of the control frequency of a system has a relevant impact on the ability of reinforcemen...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
International audienceWe consider continuous state, continuous action batch reinforcement learning w...
Abstract — We consider batch reinforcement learning problems in continuous space, expected total dis...
In the field of Reinforcement Learning, Markov Decision Processes with a finite number of states and...
We consider batch reinforcement learning problems in continuous space, expected total discounted-rew...
International audienceThis paper establishes the link between an adaptation of the policy iteration ...
International audienceThis paper establishes the link between an adaptation of the policy iteration ...
International audienceThis paper establishes the link between an adaptation of the policy iteration ...
International audienceThis paper establishes the link between an adaptation of the policy iteration ...
The choice of the control frequency of a system has a relevant impact on the ability of reinforcemen...
The choice of the control frequency of a system has a relevant impact on the ability of reinforcemen...
The choice of the control frequency of a system has a relevant impact on the ability of reinforcemen...